67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
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"""stats 包测试。
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原则（见 docs/02-architecture.md §5）：**测已知解析解，不测实现细节**。
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"""
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from __future__ import annotations
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import numpy as np
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import pytest
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pytestmark = pytest.mark.skip(reason="未实现：见 docs/03-roadmap.md L1")
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def test_bootstrap_ci_matches_normal_theory_on_large_n(rng):
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"""大样本下 bootstrap 百分位区间应逼近正态解析区间（±10% 容差）。"""
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from mcm_toolkit.stats import bootstrap_ci
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x = rng.normal(10, 2, size=5000)
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r = bootstrap_ci(x, statistic=np.mean, confidence=0.95, n_resamples=5000, random_state=0)
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se = x.std(ddof=1) / np.sqrt(x.size)
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assert abs((r.params["high"] - r.params["low"]) - 2 * 1.96 * se) < 0.1 * 2 * 1.96 * se
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def test_bootstrap_ci_works_for_arbitrary_statistic(rng):
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"""bootstrap 的核心价值：任意统计量都能给区间（这里用中位数）。"""
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from mcm_toolkit.stats import bootstrap_ci
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x = rng.lognormal(0, 1, size=2000)
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r = bootstrap_ci(x, statistic=np.median, random_state=0)
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assert r.params["low"] < r.params["high"]
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assert r.params["low"] < np.median(x) < r.params["high"]
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def test_bootstrap_ci_is_reproducible(rng):
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from mcm_toolkit.stats import bootstrap_ci
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x = rng.normal(size=500)
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a = bootstrap_ci(x, statistic=np.mean, random_state=123)
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b = bootstrap_ci(x, statistic=np.mean, random_state=123)
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assert a.params["low"] == b.params["low"]
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def test_confidence_interval_matches_scipy(rng):
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from scipy import stats
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from mcm_toolkit.stats import confidence_interval
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x = rng.normal(5, 1, size=50)
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lo, hi = confidence_interval(x, confidence=0.95, method="t")
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ref = stats.t.interval(0.95, len(x) - 1, loc=x.mean(), scale=stats.sem(x))
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assert np.allclose([lo, hi], ref, rtol=1e-6)
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def test_correlation_matrix_diagonal_is_one(rng):
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from mcm_toolkit.stats import correlation_matrix
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x = rng.normal(size=(200, 4))
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c = correlation_matrix(x)
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assert np.allclose(np.diag(np.asarray(c)), 1.0)
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def test_describe_reports_expected_keys(rng):
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from mcm_toolkit.stats import describe
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table = describe(rng.normal(size=(100, 3)))
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for key in ["mean", "std", "min", "max", "median"]:
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assert key in table.index
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